Self-supervised Learning. Due to its ability to learn from unlabeled data, self-supervised learning has been studied in different sub-fields in AI, such as in robotics [1]}, [2]}, computer vision [3]}, [4]}, machine learning [5]} and natural language processing [6]}. The majority of these methods focus on learning from fixed datasets. In robotics, since the robot can interact with the real world, we can plan and control how data is collected  [7]}, [8]}. This degree of freedom in data collection significantly boosts the self-supervised learning efficiency.
